From implicit to explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users

Quyen Tran, Lam Tran, Linh Chu Hai, Ngo Van Linh, Khoat Than

Abstract: In this work, we examine the advantages of using multiple types of behaviours in recommendation systems. Intuitively, each user often takes some implicit actions (e.g., click) before making an explicit decision (e.g., purchase). Previous studies show that implicit and explicit feedback has different roles for a useful recommendation. However, these studies either exploit implicit and explicit behaviours separately or ignore the semantics of sequential interactions between users and items. In addition, we go from the hypothesis that a user’s preferences at a time are combinations of long-term and short-term interests. In this paper, we propose some Deep Learning architectures. The first one is Implicit to Explicit (ITE), to exploit users’ interests through the sequence of their actions. The second and third ones are two versions of ITE with Bidirectional Encoder Representations from Transformers based (BERT-based) architecture called BERT-ITE and BERT-ITE-Si, which combine users’ long- and short-term preferences without and with side information to enhance users’ representations. The experimental results show that our models outperform previous state-of-the-art ones and also demonstrate our views on the effectiveness of exploiting the implicit to explicit order as well as combining long- and short-term preferences in three large-scale datasets. The source code of our paper is available at: https://github.com/tranquyenbk173/BERT_ITE.